High-resolution structures of biological molecules obtained through x-ray crystallography provide among the most important sources of data for understanding function and mechanism. However, the process of crystallization presents a major bottleneck since hundreds or thousands of conditions need to be evaluated to find crystals useful for structure determination. We propose to acquire a system for the automated visualization of crystallization experiments in order to expand the number of crystallization trials that can be effectively screened, and enhance the rate of success. Another challenge in the evaluation of crystallization trials arises from distinguishing protein crystals from crystals of unwanted by-products of the crystallization experiment, such as salts, a common problem, and detergents, or lipids that frequently complicate experiments involving membrane proteins. To address these difficulties, we have selected an instrument that includes a UV optical system, which can identify protein crystals through their absorptive properties. UV optics also enhances the detection limit of the instrument such that very small crystals (<10<m) can be identified with confidence. Columbia University has a large and vibrant structural biology community, involved in numerous projects of substantial biomedical importance, and many members play leading role in NIH-funded high-throughput crystallography projects that will directly benefit from this instrument. Finally, the images acquired with this instrument will provide a large dataset for the development of automated image analysis software currently underway at Columbia. This software will be refined to enable rapid automated optimization of crystallization conditions. After substantial due diligence, we have opted to propose the acquisition of a Formulatrix imaging system, equipped with dual UV capability, and two storage incubators enabling use at multiple temperatures. PUBLIC HEALTH RELEVANCE: High-resolution structures of biological molecules obtained through x-ray crystallography provide among the most important sources of data for understanding function and mechanism. However, the process of crystallization presents a major bottleneck since hundreds or thousands of conditions need to be evaluated to find crystals useful for structure determination. We propose to acquire a system for the automated visualization of crystallization experiments in order to enhance the rate of success.